1. Field of the Invention
This invention relates generally to property valuation and more particularly to processing transaction data in conjunction with the estimation and application of house price indices (HPI) in marking-to-market predictions.
2. Description of the Related Art
A house price index (HPI) measures the average price appreciation of residential properties in a properly defined housing market. Once it is estimated, the HPI for a market can be used to predict the value of any property located in that market, provided the property had a prior transaction. The predicted value is derived using a previous transaction for that property and the overall market appreciation implied by the market HPI. This value prediction via the estimation and the application of HPI may be called marking-to-market (MTM).
The repeated transaction index (RTI) model is a commonly recognized method for estimating HPIs. This method was proposed by Bailey et al. (1963) and popularized by the seminal work of Case and Schiller (1989) (“Case-Shiller”). The RTI model, which may also be referred to as a repeat sales model, regresses the log difference between the two transacted values of a same address on time dummies. The estimated parameters of the RTI model represent a house price index that may be a piecewise constant function of time, wherein each basic time interval (usually a calendar quarter) is deemed to have the same house price level. The RTI model may also be variously modified. For example, one might want to consider the HPI as a non-parametric and continuous function of time.
Transaction values recorded in data are often observed with errors. The errors can be classified into idiosyncratic error and systematic bias. The former results from poor data integrity. It manifests itself at the transaction level but tends to cancel out at a reasonable aggregate/cohort level. The latter is often a result of misspecification of the model or some misuse of data, and tends to remain present at the aggregate/cohort level.
Previous literature has recognized many sources of systematic bias and has proposed various ways of correcting those biases. One source of such systematic bias can be termed as aggregation bias. Because of the lack of sufficient transaction data for each local housing market, it becomes a necessity to define a very large area as one single housing market. A large geographic area may include numerous heterogeneous local neighborhoods that may differ greatly in housing price dynamics. Forcing all these neighborhoods to share the same HPI creates an aggregation bias.
One way to circumvent the aggregation bias is to seek other data sources so that one can estimate separate HPIs for smaller and more homogeneous markets. To this end, some home price estimation uses data on mortgage transactions as well as on publicly recorded deed transactions, because associated with each mortgage loan transaction there is typically a valuation of the underlying collateral. Mortgage transactions can be classified into loans for purchases and loans for refinances. Using data on refinance transactions creates another source of systematic bias embedded in different transaction types.
The best property transaction data are believed to be records of arm's length purchase transactions, wherein the transaction price reflects the results of negotiation between a buyer and a seller. It has been recognized that different loan types or segments imply different degrees of valuation bias. It is believed that valuation bias is a function of loan purpose, loan-to-value ratio, and other factors. This is referred to as transaction type bias.
Accounting for transaction type bias is well known, such as described by Stephens et al. (1995) and Chinloy et al. (1996). Existing models account for this bias but remain inadequate. One model proposes a solution of the appraisal bias specific to the refinance data. This solution purports to generally enhance the repeat sales model by taking into account this bias. However, this approach is inadequate because, among other things, it does not accommodate for different data requirements that may be needed at different levels of geographic aggregation.
The use of both purchase transaction data and mortgage refinance data in the estimation of HPI is advocated in the Office of Federal Housing Enterprise Oversight (OFHEO)'s HPI. OFHEO, a regulatory body for Government Sponsored Entities (GSE), estimates RTI based HPIs for various housing markets by collecting loan acquisition data from the two housing GSEs. The OFHEO approach implements large data stores to increase coverage and support drilling down to smaller and more homogenous housing markets. Two significant limitations to this approach are that the transaction type bias in refinance transactions may render the estimated HPI biased, and that the fluctuation in the share of refinance transactions (e.g., from mortgage interest rate swings) causes the HPI to be dominated by purchase transactions in some periods, and refinance transactions in other periods, resulting in unwanted volatility.
Another problem with estimation that involves transaction data is idiosyncratic error. Even for a purchase transaction, the recorded transaction value in the data does not always reflect the true market value for the underlying property at a given point of time. The source of this idiosyncratic error stems from (1) recording error (e.g., missing a decimal point, a mismatch for the address name or the unit number, etc.); (2) market inefficiency; and (3) outright fraudulent transaction. Additionally, duplicate records may exist for various resources. For example one typically obtains property transaction data from several sources, which may each provide a record for a given transaction.
Another problem with property valuation relates to marking-to-market, namely, how to use prior transactions and estimated HPIs to predict current property values. Conventionally, one marks-to-market a property value by using a prior sale value and the estimated local HPI to derive the current value for a given property. If there is more than one prior transaction for the same property, one has to decide which prior transaction to use for predicting the current price of the property. There are different thoughts as to which prior transaction to choose. Being mindful of the embedded transaction type bias, one school of thought advocates using only prior purchase transactions to mark-to-market. This approach sacrifices in coverage, as there are properties whose only prior transactions are non-purchase transactions. However, if there are non-purchase transactions that are significantly more recent than the purchase transaction, a practitioner might prefer to use the non-purchase transaction. Each existing practice thus has significant drawbacks.
What is needed are repeated transaction based property valuation techniques that better accommodate for systematic and idiosyncratic error, and techniques that accommodate better estimations in marking-to-market for a given property with multiple prior transactions.